Scalable compaction in a concurrent transaction processing distributed database

ABSTRACT

A distributed database compaction system can perform compaction for transactional queries in an asynchronous matter without affecting completion of the queries. The compaction system can implement asynchronous transformation of key pairs in the database, and older keys can be periodically purged using a scheduled compactor. Subsequent queries use the compacted stored keys to perform efficient queries with direct reads of committed transactions and more efficient access to key values stores of the distributed database.

PRIORITY CLAIM

This application claims the benefit of priority to U.S. ProvisionalPatent Application Ser. No. 63/233,097 filed Aug. 13, 2021, the contentsof which are incorporated herein by reference in their entirety.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to a network-baseddatabase system or a cloud data platform and, more specifically, toprocessing concurrent transactions to enable transactional processingand compaction of the transactional data in a scalable and performantmanner within the database system.

BACKGROUND

Cloud-based data warehouses and other database systems and platformssometimes provide support for transactional processing that enable suchsystems to perform operations that are not available through thebuilt-in, system-defined functions. However, transactional processing ofthe data can rapidly grow, and it can be difficult to compact the datain a secure manner that does not affect accuracy or integrity of thedata.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure.

FIG. 1 illustrates an example computing environment that includes anetwork-based database system in communication with a cloud storageplatform, in accordance with some embodiments of the present disclosure.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, in accordance with some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating components of an executionplatform, in accordance with some embodiments of the present disclosure.

FIG. 4 is a computing environment conceptually illustrating an examplesoftware architecture for managing and executing concurrent transactionson a database system, which can be performed by a given execution nodeof the execution platform, in accordance with some embodiments of thepresent disclosure.

FIG. 5 is a flow diagram of method for implementing databasetransactions, in accordance with some embodiments of the presentdisclosure.

FIG. 6 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 7 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 8 shows a compaction system, in accordance with some embodiments ofthe present disclosure.

FIG. 9 shows a flow diagram for compacting database data, in accordancewith some embodiments of the present disclosure.

FIG. 10 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description in order to provide athorough understanding of the subject matter. It will be understood thatthese examples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

In database systems, performing transactions on a given database can besupported. To facilitate that a given transaction is committed to atable, existing database systems can employ varying approaches includingOnline Transactional Processing (OLTP) techniques. As discussed herein,OLTP refers to a category of data processing that involvestransaction-oriented tasks. In an example, OLTP involves inserting,updating, and/or deleting varying amounts of data in a given database.OLTP can deal with large numbers of transactions by a large number ofusers. Increasingly, such transactions are implemented by users that areworking in a distributed and networked environment from varyinglocations and computing environments. Thus, it is also increasinglyimportant to ensure such transactions execute and complete in aconcurrent manner that protects the integrity and consistency of thedata in such a distributed environment.

As described herein, a database system provides concurrency control andisolation for executing a series of query statements (e.g., StructuredQuery Language (SQL) statements) within a transaction against alinearizable storage. In particular, the database system herein employsa concurrency control mechanism that is a combination of a multi-versionconcurrency control for read operations (MVCC) and locking for writeoperations. Additionally, the database system implements a targetedisolation level (e.g., snapshot isolation), where each statement canexecute against a different snapshot of a database, and write locks areheld until a transaction commit.

The database system, in an embodiment, implements a two-leveltransaction hierarchy, where a top-level transaction corresponds to aSQL transaction, and a nested transaction corresponds to a SQL statementwithin the parent SQL transaction. A given nested transaction canperform read and write operations, and can perform a rollback andrestart execution zero or more times before succeeding. Upon transactioncommit, write operations can become visible, and write locks held byeach contained statement can be released.

Further, embodiments of the database system address deadlock detectionand resolution for databases. Advantageously, the database system avoidsfalse positives where only transactions involved in a deadlock will beaborted. This is helpful for users to find deadlocks in theirapplication code so that deadlocks can be fixed. In addition, thedatabase system implements embodiments of distributed deadlock detectionwithout a centralized transaction manager. In an example, this isdesirable for distributed databases, where each transaction is executedby a separate job, so that the coordination among different jobs/nodesare minimized.

In some example embodiments, the database system does not remove datafrom the underlying data store, and a separate compactor is implementedto perform compaction. The compaction can track an oldest transactions'read time stamp, and then periodically sweep the database (e.g.,Foundation Database (FDB)) to find “dead” versions of the objects. Insome example embodiments, the compactor is also published in thedatabase to enable in-progress statements to remove dead versions of theobject as part of normal online execution. In some example embodiments,the compactor sweeps for objects that may be deleted and can furtherrewrite the objects to contain their commit timestamps instead of theirtransaction ID, thereby enabling compaction of transaction status table,as discussed below, and further avoiding network overhead (e.g.,checking the transaction status table for the commit result andtimestamp) for subsequent reads and writes of subsequent queries, whichsignificantly increases the performance of the subsequent queries andreduces the memory usage. For example, a subsequently received query canread the transaction status directly when reading the query data,instead of performing additional reads (e.g., from a status table).

FIG. 1 illustrates an example computing environment 100 that includes adatabase system in the example form of a network-based database system102, in accordance with some embodiments of the present disclosure. Toavoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted fromFIG. 1 . However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of thecomputing environment 100 to facilitate additional functionality that isnot specifically described herein. In other embodiments, the computingenvironment may comprise another type of network-based database systemor a cloud data platform.

As shown, the computing environment 100 comprises the network-baseddatabase system 102 in communication with a cloud storage platform 104(e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage),and a cloud credential store provider 106. The network-based databasesystem 102 is a network-based system used for reporting and analysis ofintegrated data from one or more disparate sources including one or morestorage locations within the cloud storage platform 104. The cloudstorage platform 104 comprises a plurality of computing machines andprovides on-demand computer system resources such as data storage andcomputing power to the network-based database system 102.

The network-based database system 102 comprises a compute servicemanager 108, an execution platform 110, and one or more metadatadatabases 112. The network-based database system 102 hosts and providesdata reporting and analysis services to multiple client accounts.

The compute service manager 108 coordinates and manages operations ofthe network-based database system 102. The compute service manager 108also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (alsoreferred to as “virtual warehouses”). The compute service manager 108can support any number of client accounts such as end users providingdata storage and retrieval requests, system administrators managing thesystems and methods described herein, and other components/devices thatinteract with compute service manager 108.

The compute service manager 108 is also in communication with a clientdevice 114. The client device 114 corresponds to a user of one of themultiple client accounts supported by the network-based database system102. A user may utilize the client device 114 to submit data storage,retrieval, and analysis requests to the compute service manager 108.

The compute service manager 108 is also coupled to one or more metadatadatabases 112 that store metadata pertaining to various functions andaspects associated with the network-based database system 102 and itsusers. For example, a metadata database 112 may include a summary ofdata stored in remote data storage systems as well as data availablefrom a local cache. Additionally, a metadata database 112 may includeinformation regarding how data is organized in remote data storagesystems (e.g., the cloud storage platform 104) and the local caches.Information stored by a metadata database 112 allows systems andservices to determine whether a piece of data needs to be accessedwithout loading or accessing the actual data from a storage device.

As another example, a metadata database 112 can store one or morecredential objects 115. In general, a credential object 115 indicatesone or more security credentials to be retrieved from a remotecredential store. For example, the credential store provider 106maintains multiple remote credential stores 118-1 to 118-N. Each of theremote credential stores 118-1 to 118-N may be associated with a useraccount and may be used to store security credentials associated withthe user account. A credential object 115 can indicate one of moresecurity credentials to be retrieved by the compute service manager 108from one of the remote credential stores 118-1 to 118-N (e.g., for usein accessing data stored by the storage platform 104).

The compute service manager 108 is further coupled to the executionplatform 110, which provides multiple computing resources that executevarious data storage and data retrieval tasks. The execution platform110 is coupled to storage platform 104 of the cloud storage platform104. The storage platform 104 comprises multiple data storage devices120-1 to 120-N. In some embodiments, the data storage devices 120-1 to120-N are cloud-based storage devices located in one or more geographiclocations. For example, the data storage devices 120-1 to 120-N may bepart of a public cloud infrastructure or a private cloud infrastructure.The data storage devices 120-1 to 120-N may be hard disk drives (HDDs),solid state drives (SSDs), storage clusters, Amazon S3™ storage systems,or any other data storage technology. Additionally, the cloud storageplatform 104 may include distributed file systems (such as HadoopDistributed File Systems (HDFS)), object storage systems, and the like.

As further shown, the storage platform 104 includes clock service 130which can be contacted to fetch a number that will be greater than anynumber previously returned, such as one that correlates to the currenttime. Clock service 130 is discussed further herein below with respectto embodiments of the subject system.

The execution platform 110 comprises a plurality of compute nodes. A setof processes on a compute node executes a query plan compiled by thecompute service manager 108. The set of processes can include: a firstprocess to execute the query plan; a second process to monitor anddelete cache files using a least recently used (LRU) policy andimplement an out of memory (00M) error mitigation process; a thirdprocess that extracts health information from process logs and status tosend back to the compute service manager 108; a fourth process toestablish communication with the compute service manager 108 after asystem boot; and a fifth process to handle all communication with acompute cluster for a given job provided by the compute service manager108 and to communicate information back to the compute service manager108 and other compute nodes of the execution platform 110.

In some embodiments, communication links between elements of thecomputing environment 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-Networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

The compute service manager 108, metadata database(s) 112, executionplatform 110, and storage platform 104, are shown in FIG. 1 asindividual discrete components. However, each of the compute servicemanager 108, metadata database(s) 112, execution platform 110, andstorage platform 104 may be implemented as a distributed system (e.g.,distributed across multiple systems/platforms at multiple geographiclocations). Additionally, each of the compute service manager 108,metadata database(s) 112, execution platform 110, and storage platform104 can be scaled up or down (independently of one another) depending onchanges to the requests received and the changing needs of thenetwork-based database system 102. Thus, in the described embodiments,the network-based database system 102 is dynamic and supports regularchanges to meet the current data processing needs.

During typical operation, the network-based database system 102processes multiple jobs determined by the compute service manager 108.These jobs are scheduled and managed by the compute service manager 108to determine when and how to execute the job. For example, the computeservice manager 108 may divide the job into multiple discrete tasks (ortransactions as discussed further herein) and may determine what data isneeded to execute each of the multiple discrete tasks. The computeservice manager 108 may assign each of the multiple discrete tasks toone or more nodes of the execution platform 110 to process the task. Thecompute service manager 108 may determine what data is needed to processa task and further determine which nodes within the execution platform110 are best suited to process the task. Some nodes may have alreadycached the data needed to process the task and, therefore, be a goodcandidate for processing the task. Metadata stored in a metadatadatabase 112 assists the compute service manager 108 in determiningwhich nodes in the execution platform 110 have already cached at least aportion of the data needed to process the task. One or more nodes in theexecution platform 110 process the task using data cached by the nodesand, if necessary, data retrieved from the cloud storage platform 104.It is desirable to retrieve as much data as possible from caches withinthe execution platform 110 because the retrieval speed is typically muchfaster than retrieving data from the cloud storage platform 104.

As shown in FIG. 1 , the computing environment 100 separates theexecution platform 110 from the storage platform 104. In thisarrangement, the processing resources and cache resources in theexecution platform 110 operate independently of the data storage devices120-1 to 120-N in the cloud storage platform 104. Thus, the computingresources and cache resources are not restricted to specific datastorage devices 120-1 to 120-N. Instead, all computing resources and allcache resources may retrieve data from, and store data to, any of thedata storage resources in the cloud storage platform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 108, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2 , the compute service manager 108includes an access manager 202 and a credential management system 204coupled to an access metadata database 206, which is an example of themetadata database(s) 112. Access manager 202 handles authentication andauthorization tasks for the systems described herein. The credentialmanagement system 204 facilitates use of remote stored credentials(e.g., credentials stored in one of the remote credential stores 118-1to 118-N) to access external resources such as data resources in aremote storage device. As used herein, the remote storage devices mayalso be referred to as “persistent storage devices” or “shared storagedevices.” For example, the credential management system 204 may createand maintain remote credential store definitions and credential objects(e.g., in the access metadata database 206). A remote credential storedefinition identifies a remote credential store (e.g., one or more ofthe remote credential stores 118-1 to 118-N) and includes accessinformation to access security credentials from the remote credentialstore. A credential object identifies one or more security credentialsusing non-sensitive information (e.g., text strings) that are to beretrieved from a remote credential store for use in accessing anexternal resource. When a request invoking an external resource isreceived at run time, the credential management system 204 and accessmanager 202 use information stored in the access metadata database 206(e.g., a credential object and a credential store definition) toretrieve security credentials used to access the external resource froma remote credential store.

A request processing service 208 manages received data storage requestsand data retrieval requests (e.g., jobs to be performed on databasedata). For example, the request processing service 208 may determine thedata to process a received query (e.g., a data storage request or dataretrieval request). The data may be stored in a cache within theexecution platform 110 or in a data storage device in storage platform104.

A management console service 210 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 210 may receive a request to execute a joband monitor the workload on the system.

The compute service manager 108 also includes a job compiler 212, a joboptimizer 214 and a job executor 216. The job compiler 212 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 214 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 214 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor216 executes the execution code for jobs received from a queue ordetermined by the compute service manager 108.

A job scheduler and coordinator 218 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 110. For example, jobs may beprioritized and then processed in that prioritized order. In anembodiment, the job scheduler and coordinator 218 determines a priorityfor internal jobs that are scheduled by the compute service manager 108with other “outside” jobs such as user queries that may be scheduled byother systems in the database (e.g., the storage platform 104) but mayutilize the same processing resources in the execution platform 110. Insome embodiments, the job scheduler and coordinator 218 identifies orassigns particular nodes in the execution platform 110 to processparticular tasks. A virtual warehouse manager 220 manages the operationof multiple virtual warehouses implemented in the execution platform110. For example, the virtual warehouse manager 220 may generate queryplans for executing received queries.

Additionally, the compute service manager 108 includes a configurationand metadata manager 222, which manages the information related to thedata stored in the remote data storage devices and in the local buffers(e.g., the buffers in execution platform 110). The configuration andmetadata manager 222 uses metadata to determine which data files need tobe accessed to retrieve data for processing a particular task or job. Amonitor and workload analyzer 224 oversee processes performed by thecompute service manager 108 and manages the distribution of tasks (e.g.,workload) across the virtual warehouses and execution nodes in theexecution platform 110. The monitor and workload analyzer 224 alsoredistributes tasks, as needed, based on changing workloads throughoutthe network-based database system 102 and may further redistribute tasksbased on a user (e.g., “external”) query workload that may also beprocessed by the execution platform 110. The configuration and metadatamanager 222 and the monitor and workload analyzer 224 are coupled to adata storage device 226. Data storage device 226 in FIG. 2 representsany data storage device within the network-based database system 102.For example, data storage device 226 may represent buffers in executionplatform 110, storage devices in storage platform 104, or any otherstorage device.

As described in embodiments herein, the compute service manager 108validates all communication from an execution platform (e.g., theexecution platform 110) to validate that the content and context of thatcommunication are consistent with the task(s) known to be assigned tothe execution platform. For example, an instance of the executionplatform executing a query A should not be allowed to request access todata-source D (e.g., data storage device 226) that is not relevant toquery A. Similarly, a given execution node (e.g., execution node 302-1may need to communicate with another execution node (e.g., executionnode 302-2), and should be disallowed from communicating with a thirdexecution node (e.g., execution node 312-1) and any such illicitcommunication can be recorded (e.g., in a log or other location). Also,the information stored on a given execution node is restricted to datarelevant to the current query and any other data is unusable, renderedso by destruction or encryption where the key is unavailable.

FIG. 3 is a block diagram illustrating components of the executionplatform 110, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3 , the execution platform 110 includesmultiple virtual warehouses, including virtual warehouse 1, virtualwarehouse 2, and virtual warehouse n. Each virtual warehouse includesmultiple execution nodes that each include a data cache and a processor.The virtual warehouses can execute multiple tasks in parallel by usingthe multiple execution nodes. As discussed herein, the executionplatform 110 can add new virtual warehouses and drop existing virtualwarehouses in real-time based on the current processing needs of thesystems and users. This flexibility allows the execution platform 110 toquickly deploy large amounts of computing resources when needed withoutbeing forced to continue paying for those computing resources when theyare no longer needed. All virtual warehouses can access data from anydata storage device (e.g., any storage device in cloud storage platform104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storagedevices 120-1 to 120-N shown in FIG. 1 . Thus, the virtual warehousesare not necessarily assigned to a specific data storage device 120-1 to120-N and, instead, can access data from any of the data storage devices120-1 to 120-N within the cloud storage platform 104. Similarly, each ofthe execution nodes shown in FIG. 3 can access data from any of the datastorage devices 120-1 to 120-N. In some embodiments, a particularvirtual warehouse or a particular execution node may be temporarilyassigned to a specific data storage device, but the virtual warehouse orexecution node may later access data from any other data storage device.

In the example of FIG. 3 , virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data being cached by the execution nodes. Forexample, these execution nodes do not store or otherwise maintain stateinformation about the execution node or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each includes one datacache and one processor, alternative embodiments may include executionnodes containing any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node,data that was retrieved from one or more data storage devices in cloudstorage platform 104. Thus, the caches reduce or eliminate thebottleneck problems occurring in platforms that consistently retrievedata from remote storage systems. Instead of repeatedly accessing datafrom the remote storage devices, the systems and methods describedherein access data from the caches in the execution nodes, which issignificantly faster and avoids the bottleneck problem discussed above.In some embodiments, the caches are implemented using high-speed memorydevices that provide fast access to the cached data. Each cache canstore data from any of the storage devices in the cloud storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the cache resources andcomputing resources associated with a particular execution node aredetermined when the execution node is created, based on the expectedtasks to be performed by the execution node.

Additionally, the cache resources and computing resources associatedwith a particular execution node may change over time based on changingtasks performed by the execution node. For example, an execution nodemay be assigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 110, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 110 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 110 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud storage platform 104, but each virtual warehouse has its ownexecution nodes with independent processing and caching resources. Thisconfiguration allows requests on different virtual warehouses to beprocessed independently and with no interference between the requests.This independent processing, combined with the ability to dynamicallyadd and remove virtual warehouses, supports the addition of newprocessing capacity for new users without impacting the performanceobserved by the existing users.

FIG. 4 is a computing environment 400 conceptually illustrating anexample software architecture for managing and executing concurrenttransactions on a database system (e.g., the network-based databasesystem 102), which can be performed by a given execution node of theexecution platform 110, in accordance with some embodiments of thepresent disclosure. In an embodiment, a process flow is performed by atransaction manager that is configured to manage and executetransactions as described further herein.

As shown, the transaction manager 440 is included in the compute servicemanager 108. The transaction manager 440 receives a job 410 that may bedivided into one or more discrete transactions 420-425, e.g.,transaction 0, transaction 1, transaction 2, transaction 3, and so forththrough transaction (n). In an embodiment, each transaction includes oneor more tasks or operations (e.g., read operation, write operation,database statement, user defined function, and the like) to perform. Thetransaction manager 440 receives the job at 450 and determinestransactions at 452 that may be carried out to execute the job 410. Thetransaction manager 440 is configured to determine the one or morediscrete transactions, such as transaction 0, transaction 1, transaction2, transaction 3, and so forth, based on applicable rules and/orparameters. The transaction manager 440 assigns transactions at 454.

As further shown, the transaction manager 440 is configured toconcurrently process multiple jobs that can be performed by theexecution platform 110. In an example, the transaction manager 440 canreceive a second job 430 or a third job 435, each of which includerespective discrete transactions that are to be performed on theexecution platform 110. Each of the transactions may be executedconcurrently by the execution platform 110 in which different operationsare performed (e.g., a respective read operation or write operation areexecuted from each of the transactions by the execution platform 110).

In an implementation, the job 410, including the respective transactionstherein, is carried out by the transaction manager 440 which can performthe responsibilities of a query manager (e.g., processing querystatements and operations, and the like). As shown, the transactionmanager 440 may have multiple threads, including, for example,transaction manager threads 442A, 442B, 442C, and so forth. Thetransaction manager 440 may assign the job 410, including the multiplediscrete transactions, to a particular virtual warehouse of theexecution platform 110. Based on this assignment, the transactionmanager 440 can send the job 410, including the multiple discretetransactions, to the assigned virtual warehouse for execution.Alternatively, the transaction manager 440 can send a subset of thetransactions included in the job 410 for execution by the executionplatform 110.

In an embodiment, as described further herein, the transaction manager440 can perform operations to process transactions (e.g., OLTP) that maybe executing concurrently, while handling conflicts and avoidingstarvation of resources. Further, as described further herein, thetransaction manager 440 handles conflicts between multiple transactionsand concurrency issues that can arise when multiple transactions areexecuting in parallel on the execution platform 110. As further shown,the execution platform 110 communicates with the storage platform 104,which provides a distributed database (e.g., Foundation Database (FDB),and the like), where data can be read and written in connection withperforming the transactions.

In an embodiment, the transaction manager 440 schedules and manages theexecution of transactions on behalf of a client account. The transactionmanager 440 may schedule any arbitrary SQL query included in a giventransaction. The transaction manager 440 may assume a role to schedulethe job 410 as if it is the client account rather than as an internalaccount or other special account. The transaction manager 440 may embodythe role of, for example, an account administrator or a role having the(smallest) scope necessary to complete the job 410. In an embodiment,the transaction manager 440 embodies the role that owns the object thatis the target of the job 410 (e.g., for a cluster, the table beingclustered is the target).

In an embodiment, the transaction manager 440 determines transactions at452 and assigns transactions at 454 that are to be performed to fullyexecute the job 410. In an embodiment, the transaction manager 440assigns ordering constraints to any number of the one or more discretetransactions, where applicable. Depending on the constraints of the job410, the transaction manager 440 may determine that one or more ofmultiple discrete transactions are to be serialized and executed in aparticular order.

In an embodiment, the transaction manager 440 generates a reportindicating when the job 410 is scheduled to be executed and how muchcomputing resources are estimated to be tied up executing the job 410.The transaction manager 440 may alert a client account when the job 410is being executed.

The database system provides concurrency control and isolation forexecuting transactions against (e.g., a series of SQL Statements withina SQL Transaction) against linearizable storage (e.g., a linearizablekey-value store, NoSQL database, an OLAP database or data warehouse). Atransaction as referred to herein includes a group of operationsexecuted atomically. In an example, such transactions may include readand write operations but can also include operations such as increment,decrement, compare-and-swap, and the like. Further, it is appreciatedthat linearizable storage may include any type of distributed database(e.g., Apache HBase).

The following discussion relates to transactions in a given distributeddatabase system. In an example, the transaction manager 440 utilizes alinearizable storage, provided by the storage platform 104, for managingand processing transactions as described herein. In an embodiment, thetransaction manager 440 implements a read committed model for performingtransactions. As referred to herein, a read committed model can refer toa model that ensures that all read operations performed in a giventransaction sees a consistent snapshot of the database (e.g., reading alast set of committed values that existed when the read operationcommenced), and the transaction itself successfully commits only if noupdates that the transaction has made results in write-write conflictswith any concurrent transactions.

As discussed further herein, the transaction manager 440 implements atwo-level transaction hierarchy, where a top-level transactioncorresponds to a SQL transaction, and a nested transaction correspondsto a SQL statement within the parent SQL transaction. A given nestedtransaction can perform operations, such as reads and writes, and canperform a rollback and restart execution zero or more times beforesucceeding. Upon transaction commit, write operations can becomevisible, and write locks held by each contained statement can bereleased.

As mentioned before, the subject system provides concurrency control andisolation for executing a series of SQL Statements within a SQLTransaction against a linearizable storage. As discussed further herein,a transaction manager (e.g., transaction manager 440) is configured toprovide a concurrency control mechanism that can be understood as acombination of multi-version concurrency control for read operations(MVCC) and locking for write operations. The subject system providestechniques for read committed isolation where each statement may executeagainst a different snapshot of the database (e.g., the storage platform104), with write locks held until transaction commit.

In an embodiment, the linearizable storage as described herein enableseach operation to execute atomically between invocation and response. Asan example, such a linearizable key-value store ensures that operationsexecute in an atomic manner consistent with a “real-time” ordering ofthose operations e.g., when operation A completes before operation Bbegins, operation B should take effect after operation A. In the contextof a database, a first write operation to a row in the table takeseffect before a second write or read operation to the same row in thetable if the second operation was issued after the first completed.

The examples described herein relate to linearizable storage such as alinearizable database, including, for example, NoSQL systems, and thelike. A given NoSQL database refers to a database that stores data in aformat other than a tabular format, and can store data differently thanin relational tables. Further, Uber's Schemaless is an example ofbuilding linearizable Key-Value storage via having a “key” and “value”column in a relational table. Other examples of linearizable databasesare: HBase, RocksDB, TiKV, Redis, Etcd.

Some examples of optimizations provided by the subject system includeutilizing restricted transactional capabilities offered by someembodiments of storage platform 104, such as FoundationDB, that can beleveraged to enable a more efficient transaction implementation. Forexample, in a write (/lock/delete) protocol, a write operation isperformed, and then a read operation is done to check for (1) any writeoperation that happened before the write request was submitted (2) anyother write operation was submitted concurrently with the writeoperation that was serialized before. The following example illustratesthe above:

-   -   T1 starts statement S1    -   S1 starts a FoundationDB Transaction, and uses its Read Version        as the Read Timestamp    -   S1 wishes to write object X, so it first reads object X as of        the Read Timestamp    -   Finding no conflicts, S1 writes X, using a timestamped operation        to embed the commit timestamp in the key and setting        IsCommitEmbedded.    -   S1 sets a read conflict range on the FoundationDB transaction        for all keys with a prefix of X    -   S1 writes a transaction status entry for ID, directly setting it        to committed.    -   T1 commits the FoundationDB Transaction.    -   If the transaction commits, then there were no concurrent        conflicting transactions.    -   If the transaction is aborted, then there was a concurrency        conflicting transaction for one of the writes that were done.        None of S1's writes, nor the transaction status entry will be        persisted. S1 now restarts in the slow path.

In an example, a “read version” refers to a “version” or state of thedatabase that corresponds to when a last operation was successfullycommitted to the database.

The following relates to a discussion of strict serializability. Whereaslinearizability makes a “real-time” ordering and atomicity promise aboutsingle operations, strict serializability makes a “real-time” orderingand atomicity promise about groups of operations. In an example, thegroup of operations is submitted incrementally over time, with aterminal “commit” command being issued. The strictly serializablestorage platform may employ techniques such as pessimistic lock-basedexclusion or an optimistic validation phase to enable thisfunctionality. In this example, the group of operations is referred toas a transaction as mentioned herein. The subject system can imposerestrictions on the transaction, such as the number, size, or durationof the operations, and always reject transactions that exceed theselimits.

In an embodiment, read operations may be optimized in the followingmanner. When reading with a given read timestamp, it may not be feasiblefor any transaction started after the read timestamp to commit beforethe read timestamp. Thus, if the Transaction ID is set to be the same asthe first statement's read timestamp, then instead of reading [X.0,X.inf], the subject system can read [X.0, X.readTimestamp].Consequently, this approach can make read operations for old orfrequently written data more efficient.

In an embodiment, the subject system implements a two-level transactionhierarchy, where the top-level transaction corresponds to a SQLTransaction, and the nested transaction (referred to as a“StatementContext”) corresponds to a SQL statement within the parent SQLTransaction. A given StatementContext performs read and write operationsand may be instructed to perform a rollback and restart execution zeroor more times before succeeding. In an example, transactions control thecollective visibility of all write operations from successfulstatements. Upon transaction commit, all write operations becomevisible, and all write locks held by each contained statement arereleased.

In an embodiment, each object key is associated with a stamp thatuniquely identifies a single execution attempt of a statement, which canbe by appending a three-part tuple of (Transaction ID, statementNumber,restartCount). The higher order component is the transaction identifierassigned to the SQL-level transaction. The statementNumber identifiesthe SQL statement within the SQL-level BEGIN/COMMIT block. The restartcount tracks which statement restart attempt generated this writeoperations. A StatementContext is instantiated with this stamp, andapplies it to all writes performed through the StatementContextinstance.

Stamping keys this way has a number of desirable properties. First, ifkey1<key2, then key1.suffix1<key2.suffix2, regardless of the values ofsuffix1 and suffix2. If key1==key2, then the transactionID component ofthe suffix allows us to resolve the commit status of the object todetermine its visibility to the statement. IftransactionID1==transactionID2, then Statement Number allows statementsto see writes performed by previous statements within the sametransaction. The restartCount component of the suffix enables the systemto detect and delete obsolete versions of the object that had been leftaround when a statement has to be restarted.

In a similar fashion each execution of a statement is given a three-partidentifier consisting of the statement's readTimestamp (RTS) and thecurrent values of statementNumber (SN) and restartCount (RC). Thisapproach ensures that each statement that is part of the execution of aSQL statement (or more generally a SQL Transaction), sees either datacommitted before the SQL statement started or by data written or updatedby the transaction itself.

In an embodiment, the transaction manager employs a Transaction StatusTable (TST) to keep track of committed and aborted transactions. The TSTis a persistent hashmap that maps Transaction ID to its metadata, mostnotably a list of finalized statement numbers and their final restartcount, and the commit outcome including the transaction's committimestamp (CTS). Transactions that are in progress do not exist in theTransaction Status Table. In an embodiment, the TST can be stored in thestorage platform 104, or within memory or cache of the executionplatform 110.

The following discussion relates to a read protocol that is utilized bythe transaction manager 440.

In an embodiment, the transaction manager 440 uses a read committedtransaction isolation level, and each statement may be run with adifferent read timestamp. In an example, the read request for a givenkey (or a range of keys) is implemented by executing a linearizablestorage read call for all keys with X as their prefix. The call returnsversions of X with their stamps and values. The read method returnseither the latest version of X made by a transaction that committedbefore the SQL statement started or which was written by the most recentstatement of the transaction itself that was not canceled (if any).

The following discussion relates to a write protocol that is utilized bythe transaction manager 440.

In an embodiment, the write protocol checks both for WW (write-write)conflicts and WW deadlocks. The following example describes a singletransaction and no conflicts. Assume that object X initially has a stampof TXN1.0.0 and was committed at timestamp 10. In the following example,it should be understood that the following transactional steps describedfurther below can be done within one transaction, and collectivelycommitted. On failure, or upon exceeding the limitations of theunderlying transactional system, the execution can fall back to issuingthe operations individually as described in further detail below.

T2 starts and creates S1 of StatementContext(ID=TXN2, StatementNumber=1, restartCount=0)

Assume that the constructor obtains a read timestamp from thelinearizable storage of 15 by contacting the clock service 130. Asmentioned before, the clock service 130 is a component of the storageplatform 104 which can be contacted to fetch a number that will begreater than any number previously returned, such as one that correlatesto the current time. In an embodiment, clock service 130 is providedseparately and is independently contactable from the linearizablestorage, or can be integrated into the linearizable storage such thatthe clock value may be inserted into a written value. The latteroperation will be referred to as a timestamped write.

To update value of X, the following sequence of actions is performed inan embodiment:

{  S1 does a linearizable storage write for X.TXN2.1.0 with a value of100  // The next step is for S1 to check for WW (write-write) conflictsby  checking whether there is  // another transaction that has updated Xbetween the RTS and S1's write.  S1 issues the range read [X.0, X.inf]to obtain the set all versions of X and  their stamps  The read returns[X.TXN1.0.0, X.TXN2.1.0].  S1 looks up TXN1 in the Transaction StatusTable, finds a commit  timestamp of 10.  10 is earlier than our readtimestamp of 15, so it is not a conflict.  S1 ignores [X.TXN2.1.0] as itbelongs to S1  // Assume for now, there were no conflicts detected  S1finalizes, and records (statement number=1, restart count=0) into the transaction  status table for TXN2 }

T2 commits. This will cause the Transaction Status Table record to beupdated in linearizable storage to reflect that TXN2 is now committedand its commit timestamp of 20.

At this point there will be two versions of X, one stamped with TXN1.0.0and the other TXN2.1.0. Subsequent transactions that read X candetermine if this new version of X was written by a committedtransaction by reading the transaction status record, and determine theCTS of the transaction.

The write protocol for transaction T can now be stated.

In an implementation, each row (object) updated uses two separatelinearizable storage transactions:

-   -   1) The first linearizable storage transaction of T inserts a new        version of the object with its key X suffixed with three-part        suffix (T.ID, T.statementNumber, T.restartCount).    -   2) The second linearizable storage transaction issues a range        read with the prefix “X.” to obtain the SCT (set of conflicting        transactions). The result set is a list of committed or active        transactions that wrote (or are writing) new versions of X.

There are a number of possible distinct outcomes to this linearizablestorage read call that are evaluated in the following order:

-   -   1) SCT is empty in which case T is trivially allowed to proceed.    -   2) SCT is not empty, but for all Ti in SCT, Ti has committed        before T's read timestamp, and thus are not WW (write-write)        conflicts. T may proceed.    -   3) SCT is not empty; for all Ti in SCT, Ti is committed; and        there exists a Ti in SCT, such that its CTN is greater than T's        read timestamp. T is permitted to restart without delay.    -   4) SCT is not empty, and for one or more Ti in SCT, Ti has not        yet committed or aborted. T waits for all transactions in SCT to        complete before restarting the current statement.    -   5) SCT is not empty, and for one or more Ti in SCT,        Ti.TransactionID is the same as our own transaction ID, and        Ti.StatementCount is less than our current statement count. This        means that currently the lock is held, as a previous statement        took it and successfully finished its execution. T may proceed.    -   6) SCT is not empty, and for one or more Ti in SCT,        TI.TransactionID is the same as our own transaction ID,        Ti.StatementCount is the same as our own StatementCount, and        Ti.RestartCount is less than our own restart count. This is a        lock from a previous execution of our own transaction, thus T        holds the lock on this row, and T may proceed.

For all cases, the object (X.Stamp, Value) will be left in the database(e.g., the storage platform 104). For (3) and (4) which requirerestarts, the object is left to serve as a write lock. In general, alltentative writes for an object X will form a queue of write locks. (5)and (6) illustrate the cases where previously left write locks allowsubsequent statements or restarts of a statement to recognize that theyalready hold the lock that they wish to take.

The following discussion describes an example that illustrates awrite-write (WW) conflict. A write—write conflict, which is alsounderstood as overwriting uncommitted data, refers to a computationalanomaly associated with interleaved execution of transactions. Tosimplify the example, stamps are omitted. Assume that before either T1or T2 starts that object X has a value of 500, a stamp of TXN1.0.0, anda CTN of 10.

T1 starts and gets a read timestamp of 15T2 starts and gets a read timestamp of 20T2 writes (key=X.T2, value=100)T2 issues a linearizable storage read with range [X.0, X. Inf]. The setSCT will be empty so T2 continuesT1 writes (key=X.T1, value=50)T1 issues a linearizable storage read with range [X.0, X. Inf]. The setSCT will contain T2 so T1 must restartT2 successfully commits. T1's CTN for X will be >20. Assume it is 21After waiting until T2 either commits or aborts, Ti restarts thestatement with a read TS>21.

The following discussion relates to a delete protocol utilized by thetransaction manager 440.

In an embodiment, delete operations are implemented as a write of asentinel tombstone value; otherwise, delete operations employ the sameprotocol as write operations. When a read operation determines that themost recently committed key is a tombstone, it considers that key to benon-existent.

The following discussion relates to a lock protocol utilized by thetransaction manager 440.

To support a query statement of SELECT . . . FOR UPDATE, the transactionmanager API offers StatementContext::lock(Key), which allows rows to belocked without writing a value to them. The implementation of lock( )follows the write protocol, except that it writes a special sentinelvalue to indicate the absence of a value (distinct from SQL NULL). ASELECT . . . FOR UPDATE statement may also be forced to restart severaltimes before the statement finishes successfully. Once it does,subsequent statements in the transaction will recognize the existence ofthis key as an indication that they hold the lock (in accordance withcases (5) and (6) above). All reads can ignore the key as a write.

The following discussion relates to determining whether to commit,abort, or restart a given transaction which can be determined by thetransaction manager 440.

When a transaction finishes its execution, it will either have an emptySCT, indicating that the commit can proceed, or an SCT with one or moreconflicting transactions, indicating that the transaction will need torestart.

When a statement is restarted, all writes stamped with a lowerrestartCount are left in the database (e.g., the storage platform 104)as provisional write locks for the next execution. The next execution ofthe statement might write a different set of keys. The set differencebetween the first and second execution form a set of orphaned writesthat are removed and never become visible. The statement itself may notbe relied upon to always be able to clean up its own orphaned writes, asin the event of a process crash, the location of the previous writeswill have been forgotten. Finalizing statements and recording therestart count of the successful execution promises that only the resultsof one execution will ever become visible, and permits orphaned writesto be lazily cleaned up.

A transaction is committed, and all of its writes made visible, byinserting its Transaction ID into the Transaction Status Table. Thecommit timestamp is filled in by the clock service 130 or directly bythe distributed database (e.g., FoundationDB), such that it is higherthan any previously assigned read or commit timestamps. All writes arecompleted before a statement may be finalized, and all statements arefinalized before the transaction may be committed.

A transaction is aborted by inserting its Transaction ID into theTransaction Status Table, with its transaction outcome set as aborted.The list of finalized statements and their restart counts will be resetto an empty list. The insertion into the Transaction Status Table willmake the abort outcome visible to all conflicting transactions, and allwrites performed by finalized statements may be proactively or lazilyremoved from the database (e.g., the storage platform 104).

When a statement tries to finalize with a non-empty SCT, it waits forcommit outcomes to be persisted to the Transaction Status Table for allconflicting transactions. Once all conflicting transactions havecommitted or aborted, then the transaction will begin its restartattempt.

The following discussion relates to an API (e.g., the transactionmanager API as referred to below) that can be utilized (e.g., by a givenclient device) to send commands and requests to the transaction manager440.

A SQL transaction contains a sequence of one or more SQL statements.Each SQL statement is executed as a nested transaction, as implementedby the transaction manager StatementContext class. Each transactionmanager statement itself is executed as one or more databasetransactions.

In an embodiment, the transaction manager API is divided into twoparts: 1) the data layer, which provides a read and write API to thetransaction execution processes; and 2) the transaction layer, whichprovides, to the compute service manager 108, an API to orchestrate thetransaction lifecycle. In an implementation, transactions operate at aREAD COMMITTED isolation level and implement MVCC on top of thedistributed database (e.g., storage platform 104) to avoid taking anyread locks.

Consider the following example SQL query:

Update emp.Salary=emp.Salary*1.1 where emp.Dept=“shoe”;

In an example, an instance of the StatementContext class will be createdto execute this SQL statement. The constructor contacts the linearizablestorage transaction manager to begin a linearizable storage transactionand obtain a linearizable storage STN which is then stored in thereadTimestamp variable.

The Update operation then executes across any number of execution nodes,all using the same StatementContext instance. In an example, a functionrangeRead( ) will be used to scan the base table, or an index on Dept,for the tuples to update. A series of write( ) calls will be made toupdate the salary of all matching employees.

A call to finalize( ) will return CONFLICT if the statement encounteredany conflicts during its execution, to indicate that re-execution isneeded. The key to restarts making progress is that the first executionof the statement will have the side effect of, in effect, setting writelocks on the objects being updated. This ensures that when the statementis re-executed the necessary writes locks have already been obtained andthe statement will generally (but not always).

Next, consider an example illustrating Write-Write conflicts between 3transactions:

T1 starts S1 with timestamp 10T2 starts S2 with timestamp 20T3 starts S3 with timestamp 30S1 writes XS2 writes YS3 writes ZS1 writes Y, and notes the conflict with T2S2 writes Z, and notes the conflict with T3S3 writes X, and notes the conflict with T1

In this case described above, three transactions are involved in adeadlock. Each statement believes that it should restart and wait forthe execution of the previous transaction to finish. No transaction hasthe complete information to know that it is involved in a deadlock.

Thus, when a statement fails to finalize due to conflicts, it insteadwrites its conflict set into the database (e.g., the storage platform104). These conflict sets may be read by all other transactions,allowing them to detect a cycle in the waits-for graph, indicating thatthey're involved in a deadlock.

In database systems, a deadlock can refer to a situation where two ormore transactions are waiting for one another to give up locks. As anexample, deadlocks can be handled by deadlock detection or prevention insome embodiments. The following discussion relates to example mechanismsfor handling deadlocks utilizing distributed approaches that do notrequire a centralized deadlock handling component or implementation. Forexample, in an implementation, a particular execution node, (e.g.,execution node 302-1 and the like) in the execution platform 110 canperform at least some of the following operations described below.

Deadlock detection: A basic idea of deadlock detection is to detect adeadlock after the deadlock occurs such that that a particulartransaction can be aborted. This can be done by finding cycles in await-for graph. Depending on how deadlock detection is performed,deadlock detection can be classified as:

-   -   Online detection: whenever a transaction wishes to acquire a        lock, it adds an edge to the wait-for graph. The transaction is        aborted if this new edge will cause a cycle.    -   Offline detection: the system periodically collects the pending        lock requests from all transactions to construct a wait-for        graph and perform cycle detection.

Deadlock prevention: A basic idea of deadlock prevention is to enforcesome restrictions on locking so that deadlocks can never happen. Exampletechniques include:

-   -   Timeout: a transaction is assumed to be involved in a deadlock        if its lock request cannot be granted after a certain time        period, e.g., 5 seconds.    -   Non-blocking 2PL: whenever a conflict happens, a transaction is        aborted immediately.    -   Wait-die: when a transaction Ti requests a lock that is held by        Tj, Ti is only allowed to wait if Ti is older than Tj. Otherwise        Ti is aborted immediately.    -   Wound-wait: when a transaction Ti requests a lock that is held        by Tj, Tj is aborted if Ti has a higher priority than Tj.        Otherwise, Ti will wait.

In embodiments, the database system implements a distributed database(e.g., storage platform 104) for executing distributed transactions, andutilizes locking for concurrency control where any deadlocks are handledin a distributed manner by a particular execution node executing aparticular transaction (e.g., execution node 302-1 and the like).

In some embodiments, the database system provides the following:

-   -   No false deadlocks: Deadlocks generally represent some bugs in        the user's application code. By providing accurate and        informative deadlock information, embodiments of the database        system enables a user to fix these deadlocks.    -   Distributed/decentralized deadlock handling: transaction manager        440 is designed for executing distributed transactions in the        cloud. In an embodiment, the transaction manager 440 creates one        job (with one or more execution node workers) to execute a        transaction. It can be desirable that each transaction handles        deadlocks independently without requiring a centralized        transaction manager.

The following discussion describes a deadlock detection and resolutionprotocol for the database system to meet the two aforementionedrequirements. In order to meet the goal of no false deadlocks, thedatabase system performs deadlock detection on the wait-for graph andonly aborts a transaction if it finds a cycle in the graph. To meet agoal of not utilizing a centralized transaction manager, eachtransaction (e.g., executing on a given execution node) are able toexchange wait-for information and perform deadlock detectionindependently. Further, the database system implements a deadlockdetection algorithm that is deterministic so that all transactions canunanimously agree on which transactions to abort.

In the following discussion, it is understood that statements in atransaction are executed serially e.g., one at a time. As discussedfurther below, the database system can then extend a deadlock detectionprotocol as described herein to support parallel statement execution.

In the discussion below, “transaction” and “statement” are usedinterchangeably because it is assumed that statements of a transactionwill be executed serially, e.g., one at a time. In an example, thedatabase system utilizes a deadlock detection and resolution protocolthat enables transactions to store their wait-for information into adedicated table in a distributed database (e.g., storage platform 104).A transaction waiting for conflicting transactions can periodically runa deterministic deadlock detection algorithm. If a transactiondetermines that it is a victim in a deadlock, the transaction can abortitself so that other transactions can proceed.

In some implementations, the execution platform 110 can provide deadlockhandling logic 480 (e.g., deadlock handling logic l to deadlock handlinglogic N, which can correspond respectively to each transaction 420 totransaction 425) which implements the deadlock detection and resolutionprotocol mentioned herein, and is provided or utilized by each givenexecution node that is currently executing a given transaction(s). Inanother embodiment, each deadlock handling logic can be provided to acorresponding transaction (or statement within a transaction) fordeadlock detection and resolution as described further herein.

In an embodiment, wait-for information of transactions is stored in await-for table in the distributed database (e.g., storage platform 104).The wait-for table includes a set of key-value pairs where both keys andvalues are transaction IDs. A key-value pair <Ti, Tj> means that Ti iscurrently waiting for Tj, e.g., there is an edge Ti->Tj in the wait-forgraph.

In order to satisfy the deterministic requirement, each transaction Tireports Ti->Tj only if Tj is the oldest conflicting transaction that Tiis waiting for (a transaction's age is determined by its transaction ID,e.g., a younger (e.g., newer) transaction will have a larger transactionID). By ensuring that there is at most one ongoing edge from eachtransaction, it is straightforward to see that each transaction canparticipate in at most one cycle. Thus, the youngest transaction (withthe largest transaction ID) can be aborted in each cycle todeterministically resolve deadlocks.

In some example embodiments, the compute service manager 108 furthercomprises a compaction manager 470 that is configured to compact,transform, and remove key pairs. For example, the compaction manager 470can implement a dedicated compactor and one or more online asynchronouscompactors on execution nodes of the execution platform 110, asdiscussed in further detail below with reference to FIGS. 8 and 9 .

FIG. 5 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 500 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 500 may beperformed by components of network-based database system 102, such ascomponents of the compute service manager 108 or a node in the executionplatform 110. Accordingly, the method 500 is described below, by way ofexample with reference thereto. However, it shall be appreciated thatthe method 500 may be deployed on various other hardware configurationsand is not intended to be limited to deployment within the network-baseddatabase system 102.

At operation 502, the transaction manager 440 receives a firsttransaction that is to be executed on linearizable storage.

At operation 504, the transaction manager 440 assigns a first readversion to the first transaction. The first read version indicates afirst version of the linearizable storage. Alternatively, a readtimestamp can be retrieved from a clock service (e.g., the clock service130), and a transaction identifier can be assigned to the firsttransaction where the transaction identifier corresponds to a read starttime.

At operation 506, the transaction manager 440 performs a read operationfrom the first transaction on a table in a database.

At operation 508, the transaction manager 440 determines a first commitversion identifier corresponding to first data resulting from the readoperation.

At operation 510, the transaction manager 440 determines whether aparticular write operation is included in the first transaction. If theparticular write operation is to be performed with the firsttransaction, then the transaction manager 440 proceeds to perform amethod as described below in FIG. 7 .

Alternatively, when the transaction manager 440 determines that aparticular write operation is absent from the first transaction, atoperation 512, the transaction manager 440 proceeds to execute adifferent transaction (along with forgoing performance of a commitprocess for the first transaction), which is described, in an example,in FIG. 6 below. It is appreciated that due to the concurrency oftransactions that are performed, the operations described further belowin FIG. 6 can be executed at any time during the operations described inFIG. 5 above.

FIG. 6 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 600 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 600 may beperformed by components of network-based database system 102, such ascomponents of the compute service manager 108 or a node in the executionplatform 110. Accordingly, the method 600 is described below, by way ofexample with reference thereto. However, it shall be appreciated thatthe method 600 may be deployed on various other hardware configurationsand is not intended to be limited to deployment within the network-baseddatabase system 102.

In some embodiments, the method 600 can be performed in conjunction withthe method 500 as discussed above. For example, the method 600 can beperformed after the operations of the method 500 or performedsubstantially concurrently with the method 500.

At operation 602, the transaction manager 440 receives a secondtransaction to be executed on linearizable storage.

At operation 604, the transaction manager 440 assigns the secondtransaction a second read version that indicates a second version of thelinearizable storage.

At operation 606, the transaction manager 440 performs a second readoperation from the second transaction on the table in the database.

At operation 608, the transaction manager 440 performs a second writeoperation from the second transaction on the table in the database.

At operation 610, the transaction manager 440 determines a particularcommit version identifier corresponding to second data results from thesecond read operation.

At operation 612, the transaction manager 440 completes the writeoperation in response to the particular commit version identifier beingequivalent to the first commit version identifier.

At operation 614, the transaction manager 440 assigns a second commitversion identifier to second data stored to the table from the writeoperation, the second commit version identifier corresponding to asecond version of data in the table. The second commit versionidentifier is different than the first commit version identifier.

At operation 616, the transaction manager 440 initiates a commit processfor the second transaction.

FIG. 7 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 700 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 700 may beperformed by components of network-based database system 102, such ascomponents of the compute service manager 108 or a node in the executionplatform 110. Accordingly, the method 700 is described below, by way ofexample with reference thereto. However, it shall be appreciated thatthe method 700 may be deployed on various other hardware configurationsand is not intended to be limited to deployment within the network-baseddatabase system 102.

In some embodiments, the method 700 can be performed in conjunction withthe method 500 and the method 600 as discussed above. For example, themethod 700 can be performed after the operations of the method 500 orthe method 600 (or performed substantially concurrently therewith eithermethod). At operation 702, the transaction manager 440 proceeds toperform a particular write operation from the first transaction. Atoperation 704, the transaction manager 440 determines that the firstcommit version identifier fails to match the second commit versionidentifier. At operation 706, the transaction manager 440 aborts theparticular write operation from the first transaction. At operation 708,the transaction manager 440 performs a particular read operation fromthe first transaction on the table in the database. At operation 710,the transaction manager 440 determines a particular commit versionidentifier corresponding to particular data resulting from theparticular read operation. At operation 712, the transaction manager 440retry to perform the particular write operation from the firsttransaction. At operation 714, the transaction manager 440 perform theparticular write operation in response to the particular commit versionidentifier matching the second commit version identifier. At operation716, the transaction manager 440 initiates a particular commit processfor the first transaction.

As discussed, the transaction manager 440 writes the key-value pairs inversions, and different versions can eventually become redundant. Insome example embodiments, the older versions of the pairs cannot beremoved immediately after the new ones are written, because there may betransactions still running in the transaction manager 440 that need tosee the version through multiple version concurrency control.

In some example embodiments, the transaction manager 440 can generate aKV pair in the following format when a new record is created orotherwise inserted:

[Acct Prefix|Table ID|User Key|(Txn ID, SN, RC)|WriteTs]

In the above example format, only a transaction ID (Txn ID) is stated asat the key generation time; the commit time is not yet known since it isgenerated at a later time when the transaction (e.g., transaction data)commits to the database. In some example embodiments, to determine thestatus using the format above, the Txn ID is used to read from theTransaction Status Table (TST) to find the corresponding committimestamp. As discussed above, the TST can be implemented to keep trackof committed and aborted transactions. The TST is a persistent hash mapsaved in a Foundation Database (FDB) that maps a given Transaction ID toits metadata, e.g., including a list of finalized statement numbers andrestart count, and the commit outcome including the transaction's committimestamp, in accordance with some example embodiments. If a transactionis aborted, the value will be set to a minimum commit timestamp value.In some example embodiments, the compactor system rewrites the key byreplacing the transaction ID with the Commit Timestamp, which recordswhen the transaction has been committed.

[Acct Prefix|Table ID|User Key|Commit Timestamp|WriteTs]

The compaction manager 470 can compact different types of KV pairs, indifferent cases, including:

-   -   A. Old versions not needed for MVCC (e.g., KV pair (generated in        old transactions) overwritten later by another transaction, and        no longer needed for MVCC read).    -   B. Versions in uncommitted transactions (e.g., KV pair in        aborted transactions, KV pair in crashed transactions).    -   C. Versions not visible in committed transactions (e.g., KV pair        rewritten by the same statement, KV pair written by a statement        later restarted, KV pair overwritten in the same transaction by        a different statement, KV pair used for locking in committed        transactions).    -   D. Entries in Transaction Status Table (e.g., if the TST entry        is not referenced by any key (due to no key contains its        Transaction ID), if the transaction status represented by the        TST entry has been acknowledged by GS).    -   E. Versions not needed in both pending or committed transactions        (e.g., KV pair written by an aborted statement). When the KV        pair is written, the key contains the Transaction ID. In some        example embodiments, when the transaction manager 440 checks the        readability of a pair, it consults the TST to get its Commit        Timestamp. The compaction manager 470 can rewrite the key to the        format with Commit Timestamp embedded (e.g., as in case E above,        versions not needed).    -   F. KV pairs written in committed transactions but with (Txn ID,        SN, RC) format.

FIG. 8 shows a compaction architecture 800, according to some exampleembodiments. As illustrated, the compute service manager 805 (e.g.,compute service manager 108) implements a compaction manager 810 (e.g.,compaction manager 470) to schedule a dedicated background compactiontask to be executed by the compute service in one or more executionnodes, such as a dedicated compactor 840 in the execution node 835. Insome example embodiments, the schedule dedicated compaction tasktriggers an internal SQL statement which is pushed down to the executionnode 835, with a storage definition language (SDL) via a query operatorthread, KVCompact RSO. The KVCompact RSO scans the data in a database845 (e.g., an FDB database contacted via an FDB API) and purges ortransforms (e.g., rewrites) the old versions of the keys in differentapproaches. The following are example different approaches, inaccordance with some example embodiments:

-   -   For versions of committed transactions (case C, above), the keys        can be directly deleted if the corresponding transaction is        already committed.    -   Versions of uncommitted transactions (case B) are more        straightforward—the versions are no longer needed so as long as        the compactor service knows whether the transaction is aborted        or failed.    -   The key rewriting (case E, above) is performed if the pair is        transaction committed.    -   For case A (old versions not needed by MVCC), the compaction        manager 470 implements a compaction boundary (discussed below),        as a low-water mark. In some example embodiments, older versions        are only purged when they are older than the compaction        boundary.

The Compaction Boundary: In some example embodiments, each statementhandled by the transaction manager 440 reads the data of the statementbased on its Read Timestamp (RTS), and the compaction manager 470 onlypurges the old versions if their corresponding commit timestamps aresmaller than the minimum value of all live statement's Read Timestamps(RTS) within an account (e.g., provider database account, instance),which is referred to here as the Minimum Read Timestamp (MRTS). Theversions that are equal to or larger than MRTS can still be used for theMVCC read. Thus, in accordance with some example embodiments, the MRTSfunctions as the compaction boundary when compacting.

In some example embodiments, the compaction manager 470 forces a limiton the maximum transaction lifetime (where, for example, supposing thetransaction timeout value is TT), such that the compaction manager 470is configured to safely assume that all the statements that are activestarted after Current Timestamp (CTS)— TT; and thus the following alwaystrue: CTS-TT<=MRTS, in these example embodiments.

In some example embodiments, the compaction manager 470 implementsCTS-TT as the initial compaction boundary. Although old versions may notbe garbage collected in a timely manner, the above initial compactionboundary is efficient to implement. Further, the compaction manager 470implements the compaction boundary a parameter for the compact calls sothat the compaction manager 470 can readily change the compactionboundary to MRTS from CTS-TT later. For example, if the transactionmanager 440 maintains an accurate and up-to-date MRTS, the compactionmanager 470 may do the compaction more aggressively, thereby morequickly purging the old versions once they are no longer used. In orderto achieve this, the compaction manager 470 includes an orchestratornetwork service that calculates the MRTS based on each of thetransaction manager's instance's live transactions. For example, this isdone by having a dedicated process tracking the progress of all the XPprocesses. In some example embodiments in which CurrentTimestamp-Transaction Timeout is implemented as the compaction boundary,the value of TT is set to 1 hour, and the CTS-TT is used as thecompaction boundary as well as the transaction abortion limit. Thus, inthese example embodiments, no transactions can exist more than TT andthe status of any transaction before CTS-TT has been successfullyacknowledged by the clients.

Online Asynchronous Compaction: As illustrated, and in accordance withsome example embodiments, the compaction manager 810 can implementonline asynchronous compactors, such as async compactor 820 in executionnode 815 and async compactor 830 in the execution node 825, whichexecute on the respective different nodes in parallel to performcompaction (in asynch compactor threads in each node) without affectingthe query threads that complete the query.

For example, when the transaction manager 440 executes user queries, thecompaction manager 810 can also do compaction via the async compactors(e.g., async compactor 820, async compactor 830). For example, during arange read by the transaction manager 440, a batch of KV pairs arefetched. After serving the online request to transaction processingthreads (e.g., worker query thread), these pairs are then selected forcompaction as batched. Although only two async compactors are shown inFIG. 8 , it is appreciated that the number of async compactors can scaleefficiently as increased numbers of queries are received. In someexample embodiments, whenever the KV pairs are processed by onlineserving worker thread(s), the KV pairs are also offloaded to thecompaction threads of the compactors (e.g., async compactor 820, asynccompactor 830), for asynchronous garbage collection and transformation(e.g., to commitTs embedded format). In this way, the online asynccompactors can effectively and efficiently purge and/or rewrite the keysin batches, without impairing online OLTP performance (e.g., the user'squery can complete early, before the on-the-fly compaction, withoutbeing affected by the compaction).

In some example embodiments, when a transaction commits or aborts, someversions can be purged immediately (e.g., those associated withtransactions that have known outcomes: cases B and C). Further, as thetransaction manager 440 implements a distributed execution model,statements within a transaction could be executed on different XPprocesses. Further, since tracking all the writes for a specifictransaction at a single place can be expensive, in some exampleembodiment compaction is not performed after a transaction terminates.

In some example embodiments, the following instructions implement thecompaction steps to purge keys and/or rewrite keys. In some exampleembodiments, the following instructions are implemented as anapplication programming interface (API) of the transaction manager 440.The callers of the instructions below (e.g., online asynchronouscompactors, dedicated and scheduled compactor) pass the input to theinstructions in a valid format to run (e.g., compaction boundary iscorrect, pairs belong to same table, etc.).

::::::::CODE BEGIN:::::::: void compact_(const KVPairs & pairs, constTimestamp boundary, boolean purgeObsolete)  {   1. Traverse the KVPairsand for each KVpair:     a. Read the Transaction ID from the TxnKey andconsult the TST or      TST cache.     b. If the transaction is live(not committed or aborted), skip this pair.     c. Put the Key into thedeletion list, if       i. If the transaction is aborted or crashed,      ii. If the transaction is committed, and its IsLockflag is set,      iii. If purgeObsolete is true, the transaction is committed, the        IsTombstone flag is set and its commit timestamp is less than        the compaction boundary (see “tombstone” discussed above).      iv. If the statement restart count is not the maximum RC for the        statement     d. If the transaction is committed, and the key isnot the only version      for the user key, check the next version forthe same user key, put      the Key into the deletion list,       i. Ifthe next version in the same statement / transaction,         and it isan old version       ii. If purgeObsolete is true and the next versionis in a         different transaction, and its commit timestamp is lessthan         the compaction boundary.     e. If the key is not put intothe deletion list and the key is in      the format with (TransactionID, SN, RC) tuple, read the      transaction's Commit Timestamp“commitT” and generate the new      key format, that includes thecommitT. Further, put the old key and      the new Key with the Value asKV pairs into an update map for      efficient processing withoutrequiring access to the TST for the      commitT.     f. If the key isnot put into the deletion list and it is in the      format of CommitTimestamp, skip this pair   2. For each key in the deletion list, deletethe corresponding FDB    key by calling KeyValueStore::deleteTuple   3.For each entry in the update map, insert the new KV pairs first and then   delete the old key.  } ::::::::CODE END::::::::

In some example embodiments, the Transaction Status Table is purged tocompact the table to reduce the overhead when reading from the table. Insome example embodiments, the TST is compacted by purging keys withTransaction IDs that have already been rewritten and have existingrewritten versions (e.g., with commitTs replacing the Txn IDs). In someexample embodiments, the dedicated compactor 840 implements thefollowing instructions to perform the compaction:

::::::::CODE BEGIN::::::::  void compactTxnStatusTable_(Timestampboundary)  {   1. Scan the TST Table for TxnStatusKey and TxnStatusValuepairs   2. Put the Txn Status Key into the deletion list, if CommitTimestamp is less    than boundary   3. Traverse the deletion list andcall deleteTuple for each key  } ::::::::CODE END::::::::

In some example embodiments, the TST is cached using TST cache support.The compactTxnStatusTable_call will delete the entries in TST but theentries in TST cache are not deleted from the cache, because it shouldbe guaranteed that no KV pairs with a Commit Timestamp less than theboundary need to consult the TST. Thus the deleted Txn Status Key willnever be read in the TST cache, and will eventually be removed by theTST cache eviction or rebuild.

Online Asynchronous compaction caller(s):

In some example embodiments, during read and readRange calls by thetransaction manager 440, the compaction manager 470 calls compact usingthe read its own Read Timestamp-TT as the compaction boundary, where theKV pairs are saved in the KVCursor buffer as the pairs input forasynchronous compaction. In some example embodiments, the asynchronouscompactors run as internal tasks of which the customer is not aware isoccurring (e.g., end user submitting a given query does not initiate theasync compactors, which run in the background upon the query beingsubmitted). The asynchronous compactors perform transformation andcompaction of keys, but do not clean the entries in TST and do not callcompactTxnStatusTable. In some example embodiments, to ensure thatcompaction works correctly, even if the range query results spanmultiple batches in the KVCursor, the last pair from the previous batchis added with the current batch as the pairs to be compacted via thecompact call.

Dedicated compaction caller: In some example embodiments, a dedicatedcompactor 840 runs as a thread in one or more of the execution nodes,such as execution node 835. The dedicated compactor 840 can purge allthe cases and also rewrite the key format (to include commit timestamp).In some example embodiments, the compaction workflow for a table isimplemented by the dedicated compactor 840 as follows:

1. The dedicated compactor 840 requests and receives the list of tablesfrom the compute service manager 805.2. The dedicated compactor 840 reads data from the database 845 (e.g.,FDB API of an FDB database) for current timestamp (CTS) values andcalculates the compaction boundary as (CTS-TT).3. For each table, the KeyValueStore's read range API is called to fetcha batch of KV pairs.4. Call compact for each KV pairs batch (see “void compact [ . . . ]”above).5. If the call compact succeeds, call compactTxnStatusTable_ using CTS—TT as the boundary, thereby compacting the TST (e.g., by purging).

FIG. 9 shows a flow diagram of a method 900 for performing compaction ina database, according to some example embodiments.

At operation 905, a database system receives transactions. For example,the transactions are received from jobs 410 and processed using thetransaction manager 440 using one or more execution nodes (e.g., XPnodes).

At operation 910, one or more asynchronous compactors in execution nodestransform the keys in batches and purges the old keys. For example, byreplacing the transaction ID with a commit timestamp for each key in abatch, and deletes the old keys in the transaction ID format, across allasynchronous compactor execution nodes in parallel.

At operation 915, the transaction results (e.g., reads or writes) arecompleted using separate transaction threads that are not affected byasynchronous compaction threads. For example, a user's query thread of anode may complete (e.g., read and return data, write data) before orafter the asynchronous compaction thread of the node is complete.

At operation 920, the dedicated compactor 840 is initiated. For example,the dedicated compactor 840 is periodically initiated, e.g., once anhour, to compact every KV table in the database.

At operation 925, the dedicated compactor 840 transforms and purges thekeys. For example, the dedicated compactor 840 rewrites the keys tocommit timestamp embedded format and deletes the original keys with thetransaction IDs.

At operation 930, the dedicated compactor 840 compacts the transactionstatus table. For example, the dedicated compactor 840 calls atransaction status table compaction function that purges, from the TST,keys that have been rewritten (e.g., rewritten by the async compactor820 and async compactor 830, rewritten by the dedicated compactor 840).For example, as the commit timestamp is in the key, the TST no longerneeds to store the corresponding transaction ID for the transaction. Assuch, the batches of transactions in the TST can be purged and the TSTcan thereby be significantly compacted.

At operation 935, subsequent transactions of new queries are receivedand performed by transaction manager 440. For example, the new queriescan be more efficiently performed by reading the commit status directlyfrom the query (e.g., embedded commit timestamp in the transformed key).In some example embodiments, each query plan of the new queries isconfigured to use the commit timestamp in the key, instead of requestingdata from the status table. For instance, the query is still constructedby the querying user in the same way (e.g., same query statements aswhen the key is in the Txn ID format and the TST must be checked), butthe execution handling of the query is modified to read all the keyvalue pairs for a user case, determine whether any of the keys containsa commit timestamp, and use the commit timestamp for the query, insteadof checking the TST. In some example embodiments, if a key contains twocommitTs, the transaction manager 440 selects one for the current committimestamp (e.g., more recent commitT). In some example embodiments, theexecution handling of the compaction manager 470 implements ReadCommitted isolation level, in which each query can only access datacommitted before the query began (not the transaction began). Forexample, where:

1. T1 insert a key value pair <k1, v1> and committed at s1;2. T2 update this pair to <k1, v2> and committed at s2;3. T3 update this pair to <k1, v3> and committed at s4; and also where,4. Another transaction T4 started at s3, and try to read k1's value,then, in some example embodiments, the execution handling checks thetransaction's commitT values and returns v2 for the T4 transaction.

FIG. 10 illustrates a diagrammatic representation of a machine 1000 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1000 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 10 shows a diagrammatic representation of the machine1000 in the example form of a computer system, within which instructions1016 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1000 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1016 may cause the machine 1000 to execute anyone or more operations of method 900. As another example, theinstructions 1016 may cause the machine 1000 to implement portions ofthe data flows illustrated in at least FIG. 4 . In this way, theinstructions 1016 transform a general, non-programmed machine into aparticular machine 1000 (e.g., the compute service manager 108 or a nodein the execution platform 110) that is specially configured to carry outany one of the described and illustrated functions in the mannerdescribed herein.

In alternative embodiments, the machine 1000 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1000 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1000 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1016, sequentially orotherwise, that specify actions to be taken by the machine 1000.Further, while only a single machine 1000 is illustrated, the term“machine” shall also be taken to include a collection of machines 1000that individually or jointly execute the instructions 1016 to performany one or more of the methodologies discussed herein.

The machine 1000 includes processors 1010, memory 1030, and input/output(I/O) components 1050 configured to communicate with each other such asvia a bus 1002. In an example embodiment, the processors 1010 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 1012 and aprocessor 1014 that may execute the instructions 1016. The term“processor” is intended to include multi-core processors 1010 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 1016 contemporaneously. AlthoughFIG. 10 shows multiple processors 1010, the machine 1000 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 1030 may include a main memory 1032, a static memory 1034,and a storage unit 1036, all accessible to the processors 1010 such asvia the bus 1002. The main memory 1032, the static memory 1034, and thestorage unit 1036 store the instructions 1016 embodying any one or moreof the methodologies or functions described herein. The instructions1016 may also reside, completely or partially, within the main memory1032, within the static memory 1034, within machine storage medium 1038of the storage unit 1036, within at least one of the processors 1010(e.g., within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1000.

The I/O components 1050 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 1050 thatare included in a particular machine 1000 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1050 mayinclude many other components that are not shown in FIG. 10 . The I/Ocomponents 1050 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1050 mayinclude output components 1052 and input components 1054. The outputcomponents 1052 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 1054 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1050 may include communication components 1064operable to couple the machine 1000 to a network 1080 or devices 1070via a coupling 1082 and a coupling 1072, respectively. For example, thecommunication components 1064 may include a network interface componentor another suitable device to interface with the network 1080. Infurther examples, the communication components 1064 may include wiredcommunication components, wireless communication components, cellularcommunication components, and other communication components to providecommunication via other modalities. The devices 1070 may be anothermachine or any of a wide variety of peripheral devices (e.g., aperipheral device coupled via a universal serial bus (USB)). Forexample, as noted above, the machine 1000 may correspond to any one ofthe compute service manager 108 or the execution platform 110, and thedevices 1070 may include the client device 114 or any other computingdevice described herein as being in communication with the network-baseddatabase system 102 or the cloud storage platform 104.

The various memories (e.g., 1030, 1032, 1034, and/or memory of theprocessor(s) 1010 and/or the storage unit 1036) may store one or moresets of instructions 1016 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions 1016, when executed by theprocessor(s) 1010, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple non-transitory storage devices and/or non-transitory media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store executable instructions and/or data. The termsshall accordingly be taken to include, but not be limited to,solid-state memories, and optical and magnetic media, including memoryinternal or external to processors. Specific examples of machine-storagemedia, computer-storage media, and/or device-storage media includenon-volatile memory, including by way of example semiconductor memorydevices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM),field-programmable gate arrays (FPGAs), and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms“machine-storage media,” “computer-storage media,” and “device-storagemedia” specifically exclude carrier waves, modulated data signals, andother such media, at least some of which are covered under the term“signal medium” discussed below.

In various example embodiments, one or more portions of the network 1080may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 1080 or a portion of the network1080 may include a wireless or cellular network, and the coupling 1082may be a Code Division Multiple Access (CDMA) connection, a GlobalSystem for Mobile communications (GSM) connection, or another type ofcellular or wireless coupling. In this example, the coupling 1082 mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

The instructions 1016 may be transmitted or received over the network1080 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1064) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1016 may be transmitted or received using a transmission medium via thecoupling 1072 (e.g., a peer-to-peer coupling) to the devices 1070. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1016 for execution by the machine 1000, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the method 500 may be performed by one or moreprocessors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but also deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environment,or a server farm), while in other embodiments the processors may bedistributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

Example 1. A method comprising: receiving, by a distributed database, aplurality of transactional queries against a key-value databasecomprising data managed by key-value pairs; performing, usingasynchronous threads of execution nodes of the distributed database,asynchronous transformation and compaction of key-value pairs of datathat corresponds to the plurality of transactional queries, theasynchronous threads transforming the key-value pairs from an initialformat to a commit format that includes a commit time of data committedto the distributed database, the one or more asynchronous threadscompacting the key-value pairs by deleting the key-value pairs in theinitial format; processing, using transactional threads in the executionnodes, the plurality of transactional queries to generate results data,the asynchronous threads performing the asynchronous transformation andcompaction while the transactional threads generate the results data;and storing the results data.

Example 2. The method of example 1, further comprising: receiving aplurality of additional transactional queries against the distributeddatabase.

Example 3. The method of any of examples 1 or 2, further comprising:processing, by the transactional threads, the plurality of additionaltransactional queries using the key-value pairs in the commit time form,the transactional threads using commit times in the key-value pairs togenerate additional results data; and storing the additional resultsdata.

Example 4. The method of any of examples 1-3, wherein the distributeddatabase comprises a transaction status table that indicates whetherdata of transactions has committed to the database.

Example 5. The method of any of examples 1-4, wherein queries for datahaving key-value pairs in the initial format are executed by identifyingtransaction identifiers in the key-value pairs and access thetransaction status table to determine commit statuses.

Example 6. The method of any of examples 1-5, further comprising:performing, using a dedicated compactor thread in one of the executionnodes, transformation and compaction of the key-value pairs from theinitial format to the commit format.

Example 7. The method of any of examples 1-6, further comprising:performing, using the dedicated compactor thread, compaction of thetransaction status table by deleting key-value pairs in the transactionstatus table that have corresponding key-value pairs in the commitformat.

Example 8. The method of any of examples 1-7, wherein each key-valuepair in the initial format includes a transaction identifier value.

Example 9. The method of any of examples 1-8, wherein the asynchronousthreads perform transformation by rewriting each key-value pair suchthat the commit time replaces the transaction identifier value.

Example 10. The method of any of examples 1-9, wherein the key-valuepairs having transaction identifiers are deleted by the asynchronousthreads.

Example 11. A system comprising: one or more processors of a machine;and a memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations to implement any ofthe methods of examples 1-10.

Example 12. A machine storage medium embodying instructions that, whenexecuted by a machine, cause the machine to perform operations toimplement any of the methods of examples 1-10.

What is claimed is:
 1. A method comprising: receiving, by a distributeddatabase, a plurality of transactional queries against a key-valuedatabase comprising data managed by key-value pairs; performing, usingasynchronous threads of execution nodes of the distributed database,asynchronous transformation and compaction of key-value pairs of datathat corresponds to the plurality of transactional queries, theasynchronous threads transforming the key-value pairs from an initialformat to a commit format that includes a commit time of data committedto the distributed database, the one or more asynchronous threadscompacting the key-value pairs by deleting the key-value pairs in theinitial format; processing, using transactional threads in the executionnodes, the plurality of transactional queries to generate results data,the asynchronous threads performing the asynchronous transformation andcompaction while the transactional threads generate the results data;and storing the results data.
 2. The method of claim 1, furthercomprising: receiving a plurality of additional transactional queriesagainst the distributed database.
 3. The method of claim 2, furthercomprising: processing, by the transactional threads, the plurality ofadditional transactional queries using the key-value pairs in the committime form, the transactional threads using commit times in the key-valuepairs to generate additional results data; and storing the additionalresults data.
 4. The method of claim 1, wherein the distributed databasecomprises a transaction status table that indicates whether data oftransactions has committed to the database.
 5. The method of claim 4,wherein queries for data having key-value pairs in the initial formatare executed by identifying transaction identifiers in the key-valuepairs and access the transaction status table to determine commitstatuses.
 6. The method of claim 4, further comprising: performing,using a dedicated compactor thread in one of the execution nodes,transformation and compaction of the key-value pairs from the initialformat to the commit format.
 7. The method of claim 6, furthercomprising: performing, using the dedicated compactor thread, compactionof the transaction status table by deleting key-value pairs in thetransaction status table that have corresponding key-value pairs in thecommit format.
 8. The method of claim 1, wherein each key-value pair inthe initial format includes a transaction identifier value.
 9. Themethod of claim 1, wherein the asynchronous threads performtransformation by rewriting each key-value pair such that the committime replaces the transaction identifier value.
 10. The method of claim1, wherein the key-value pairs having transaction identifiers aredeleted by the asynchronous threads.
 11. A system comprising: one ormore processors of a machine; and a memory storing instructions that,when executed by the one or more processors, cause the machine toperform operations comprising: receiving, by a distributed database, aplurality of transactional queries against a key-value databasecomprising data managed by key-value pairs; performing, usingasynchronous threads of execution nodes of the distributed database,asynchronous transformation and compaction of key-value pairs of datathat corresponds to the plurality of transactional queries, theasynchronous threads transforming the key-value pairs from an initialformat to a commit format that includes a commit time of data committedto the distributed database, the one or more asynchronous threadscompacting the key-value pairs by deleting the key-value pairs in theinitial format; processing, using transactional threads in the executionnodes, the plurality of transactional queries to generate results data,the asynchronous threads performing the asynchronous transformation andcompaction while the transactional threads generate the results data;and storing the results data.
 12. The system of claim 11, the operationsfurther comprising: receiving a plurality of additional transactionalqueries against the distributed database.
 13. The system of claim 12,the operations further comprising: processing, by the transactionalthreads, the plurality of additional transactional queries using thekey-value pairs in the commit time form, the transactional threads usingcommit times in the key-value pairs to generate additional results data;and storing the additional results data.
 14. The system of claim 11,wherein the distributed database comprises a transaction status tablethat indicates whether data of transactions has committed to thedatabase.
 15. The system of claim 14, wherein queries for data havingkey-value pairs in the initial format are executed by identifyingtransaction identifiers in the key-value pairs and access thetransaction status table to determine commit statuses.
 16. The system ofclaim 14, the operations further comprising: performing, using adedicated compactor thread in one of the execution nodes, transformationand compaction of the key-value pairs from the initial format to thecommit format.
 17. The system of claim 16, the operations furthercomprising: performing, using the dedicated compactor thread, compactionof the transaction status table by deleting key-value pairs in thetransaction status table that have corresponding key-value pairs in thecommit format.
 18. The system of claim 11, wherein each key-value pairin the initial format includes a transaction identifier value.
 19. Thesystem of claim 11, wherein the asynchronous threads performtransformation by rewriting each key-value pair such that the committime replaces the transaction identifier value.
 20. The system of claim11, wherein the key-value pairs having transaction identifiers aredeleted by the asynchronous threads.
 21. A machine storage mediumcomprising: receiving, by a distributed database, a plurality oftransactional queries against a key-value database comprising datamanaged by key-value pairs; performing, using asynchronous threads ofexecution nodes of the distributed database, asynchronous transformationand compaction of key-value pairs of data that corresponds to theplurality of transactional queries, the asynchronous threadstransforming the key-value pairs from an initial format to a commitformat that includes a commit time of data committed to the distributeddatabase, the one or more asynchronous threads compacting the key-valuepairs by deleting the key-value pairs in the initial format; processing,using transactional threads in the execution nodes, the plurality oftransactional queries to generate results data, the asynchronous threadsperforming the asynchronous transformation and compaction while thetransactional threads generate the results data; and storing the resultsdata.
 22. The machine storage medium of claim 21, the operations furthercomprising: receiving a plurality of additional transactional queriesagainst the distributed database.
 23. The machine storage medium ofclaim 22, the operations further comprising: processing, by thetransactional threads, the plurality of additional transactional queriesusing the key-value pairs in the commit time form, the transactionalthreads using commit times in the key-value pairs to generate additionalresults data; and storing the additional results data.
 24. The machinestorage medium of claim 21, wherein the distributed database comprises atransaction status table that indicates whether data of transactions hascommitted to the database.
 25. The machine storage medium of claim 24,wherein queries for data having key-value pairs in the initial formatare executed by identifying transaction identifiers in the key-valuepairs and access the transaction status table to determine commitstatuses.
 26. The machine storage medium of claim 24, the operationsfurther comprising: performing, using a dedicated compactor thread inone of the execution nodes, transformation and compaction of thekey-value pairs from the initial format to the commit format.
 27. Themachine storage medium of claim 26, the operations further comprising:performing, using the dedicated compactor thread, compaction of thetransaction status table by deleting key-value pairs in the transactionstatus table that have corresponding key-value pairs in the commitformat.
 28. The machine storage medium of claim 21, wherein eachkey-value pair in the initial format includes a transaction identifiervalue.
 29. The machine storage medium of claim 21, wherein theasynchronous threads perform transformation by rewriting each key-valuepair such that the commit time replaces the transaction identifiervalue.
 30. The machine storage medium of claim 21, wherein the key-valuepairs having transaction identifiers are deleted by the asynchronousthreads.